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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)
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Binding: Hardcover
Dewey Decimal Number: 006.31
EAN: 9780262194754
Edition: 1st
ISBN: 0262194759
Label: The MIT Press
Manufacturer: The MIT Press
Number Of Items: 1
Number Of Pages: 644
Publication Date: December 15, 2001
Publisher: The MIT Press
Studio: The MIT Press
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Editorial Review:
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMsâ-kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.
Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.
Customer Reviews
Average Rating: 
Rating: - Complete SVM Guide
Excellent theory on SVMs and VC dimensionality. However, I found the chapters on optimization a bit terse. Otherwise, an essential reference for those interested in using SVMs in classification and regression.
Rating: - machine learning via support vector machines and kernels
The authors are young researchers who did their Ph.D. research in this rapidly developing branch of pattern recognition. Because they are young and are at the state of the art in the filed the book has sevral advantages and disadvantages and what I see as a disadvantage someone else might view as an advantage. Anyway here is my view.
Advantage 1: Pattern recognition is a field of many disciplines. It has been studied by statisticians, mathematician, probabilists and engineering and people that ... Read More
Rating: - Excellent overview of the theory of kernel-based methods
This book is at the right level if you are already strong in Machine Learning theory. (e.g. Tom Mitchell's "Machine Learning").
Note that it is already getting somewhat dated. It for example includes little information on kernels for discreate structured input, such as trees and graphs.
Rating:  - In depth review of kernel methods in machine learning
Great book, but a word of caution, it is not for the novice.
Book assumes a lot of background in functional analysis and
probability. True, it has extensive appendixes but they are
short-handing the relevant materials only. However, having said
that, this is a book worth struggling with even if you have not
yet got the intuitions in the above mentioned disciplines.
It is worthwhile (at least as I can tell) to read the book
skipping the tool chapters ... Read More
Rating: - best book of kernel methods
It is the best book on kernel methods. It covers a wide range of subjects.
The best thing is that after finishing one or two basic chapters, you can read the rest of the book in any order; most chapters are almost independent to each other. At the beginning of a chapter, the authors list the prerequistites, so a reader knows whether he will be able to understand the chapter.
For now the book still reflects the state of art. But it is a fast changing field. I hope the authors will update the ... Read More
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